Evolutionary search for optimal fuzzy C-means clustering

Hruschka, Eduardo R., Campello, Ricardo J.G.B., and De Castro, Leandro N. (2004) Evolutionary search for optimal fuzzy C-means clustering. In: Proceedings of the 2004 IEEE International Conference on Fuzzy Systems. pp. 685-690. From: 2004 IEEE International Conference on Fuzzy Systems, 25-29 July 2004, Budapest, Hungary.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: http://dx.doi.org/10.1109/FUZZY.2004.137...


This paper introduces an evolutionary approach to automatically determine the optimal number and location of prototypes for the well-known Fuzzy C-Means (FCM) clustering algorithm. This approach is based on a Clustering Genetic Algorithm (CGA) specially designed for clustering tasks. It uses context-sensitive genetic operators to globally explore the search space in such a way that the strong dependence of the FCM algorithm on adequate previous estimations of the number and initial positions of its cluster prototypes is avoided. In this case, FCM works as a local search engine to speed up convergence and improve accuracy of the overall evolutionary procedure. Two examples are presented to illustrate that the proposed algorithm is able to automatically find adequate clusterings either starting from underestimated or overestimated initial number of clusters.

Item ID: 47609
Item Type: Conference Item (Research - E1)
ISBN: 978-0-7803-8353-1
Funders: CNPq
Projects and Grants: CNPq Grant 301353/03-4, CNPq Grant 540396/01-0, CNPq Grant 304724/03-3
Date Deposited: 08 Mar 2017 07:40
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
Downloads: Total: 1
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page